MD simulations based on (semi)empirical interatomic potentials are not affected
by these limitations, as the computing time for each MD step is generally more
than five orders of magnitude lower than with first-principles methods.
Interatomic potentials, however, are lengthy and cumbersome to develop, have
very limited flexibility and transferability, and generally do not offer sufficient
accuracy [1]} even in the case of simple systems [2]}.
Machine-learned force fields (MLFFs) have recently emerged as an ideal solution
to drastically augment the length and time scales accessible to MD simulations
while still retaining first-principles accuracy [3]}, [4]}.
Machine-learning models based on mapping the local environment around each atom
in the system onto a set of descriptors allow to train force fields that reproduce
first-principles energies, forces, and stresses with very small errors.
A key issue is the construction of the training sets. To avoid expensive
and somewhat manual procedures for building a training database, so-called active
learning schemes are becoming increasingly popular [5]}, [6]}, [7]}, [8]}. In particular, a number of algorithms have been put
forward in order to generate potentials on the fly, relying on query strategies to
allow the machine to judge whether new structures need to be added to the training
dataset. An active learning strategy where structures are generated on the fly
during MD simulations, combined with Bayesian inference to estimate the
uncertainty of the machine-learning model, has been shown to be especially
flexible and efficient, and it can be seamlessly integrated into existing
first-principles codes [7]}, [10]}.
